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Tkachenko, Nataliya, Jarvis, Stephen A. and Procter, Rob (2017) Predicting floods with Flickr tags. PLoS One, 12 (2). e0172870. doi:10.1371/journal.pone.0172870
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Official URL: http://dx.doi.org/10.1371/journal.pone.0172870
Abstract
Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags ‘#Sandy’ or ‘#flooding’), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | G Geography. Anthropology. Recreation > GB Physical geography H Social Sciences > HM Sociology Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
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| Divisions: | Faculty of Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Natural disaster warning systems, Floods, Online social networks, Metadata, User-generated content | ||||||||
| Journal or Publication Title: | PLoS One | ||||||||
| Publisher: | Public Library of Science | ||||||||
| ISSN: | 1932-6203 | ||||||||
| Official Date: | 24 February 2017 | ||||||||
| Dates: |
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| Date of first compliant deposit: | 27 February 2017 | ||||||||
| Volume: | 12 | ||||||||
| Number: | 2 | ||||||||
| Article Number: | e0172870 | ||||||||
| DOI: | 10.1371/journal.pone.0172870 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | Engineering and Physical Sciences Research Council (EPSRC) | ||||||||
| Grant number: | EP/L016400/1 (EPSRC) |
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